Consumer complaint data in the state of California in the year 2020
This chart displays the top three companies that customers filed complaints for in the state of California
This chart displays the top 5 most common issues customers filed complaints for in the state of California
The following chart depicts the companies with the most complaints filed under “debt collection” in the state of California
---
title: "Consumer Complaint Data"
author: "Diana Rodriguez"
date: "5/8/2022"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
---
Consumer complaint data in the state of California in the year 2020
```{r setup, include=FALSE}
library(tidyverse)
library(lubridate)
library(janitor)
library(readxl)
library(writexl)
library(viridis)
library(dplyr)
library(stringr)
library(plotly)
library(tigris)
library(sf)
library(tmap)
library(tmaptools)
library(htmltools)
library(janitor)
library(rmapshaper)
library(here)
library(flexdashboard)
library(DT)
options(tigris_class = "sf")
```
```{r include=FALSE}
complaints_raw <- read_rds("data/complaints.rds")
complaints <- complaints_raw %>%
clean_names()
```
```{r include=FALSE}
complaints %>%
filter(state == "CA") %>%
count(issue,sort = TRUE) %>%
arrange(desc(issue))
```
##
# Consumer Complaint Data California 2020
## Top Company Complaints in California
> This chart displays the top three companies that customers filed complaints for in the state of California
```{r, fig.width = 10, fig.height = 3}
CA <- complaints %>%
filter(state == "CA")
CA_companies <- CA %>%
group_by(company, issue) %>%
count(issue, sort = TRUE)
Top_CA_companies <- head(CA_companies,5)
Top_co <-ggplot(Top_CA_companies, aes(x = reorder(company,issue), y = n))+
geom_col(color = "#FD8F6B", fill = "#FD8F6B")+
theme(axis.text.x.left = element_text(size = .05))+
coord_flip()+
scale_y_continuous(name = "amount of complaints", labels = scales::comma) +
scale_x_discrete(name = "companies") +
labs(title = "California Company Complaints",
subtitle = "2020") +
theme_minimal()
ggplotly(Top_co)
```
## Most Common Categories of Complaints
> This chart displays the top 5 most common issues customers filed complaints for in the state of California
```{r, fig.width = 10, fig.height = 3}
CA_complaints <- CA %>%
group_by(issue) %>%
count(issue, sort = TRUE)
Top_complaints <-
head(CA_complaints,5)
top_is <- ggplot(Top_complaints, aes(x = reorder(issue,n), y = n))+
geom_col(color = "#46A5E5", fill = "#46A5E5")+
theme(axis.text.x.left = element_text(size = 2))+
coord_flip()+
scale_y_continuous(name = "Number of Complaints ", labels = scales::comma) +
scale_x_discrete(name = "Kinds of Complaints") +
labs(title = "California Common Complaints",
subtitle = "2020") +
theme_minimal()
ggplotly(top_is)
```
-------------------------------------------------------------------------------
## Analyzing Debt Collection Complaints x Companies
> The following chart depicts the companies with the most complaints filed under "debt collection" in the state of California
```{r, fig.width = 10, fig.height = 3}
CA_debt <- CA %>%
filter(product == "Debt collection") %>%
group_by(company, issue) %>%
summarise(issue) %>%
count(issue, sort = TRUE)
Top_CA_debt_complaints <-
head(CA_debt,10)
Top_db <-ggplot(Top_CA_debt_complaints, aes(x = reorder(company,n), y = n))+
geom_col(color = "#F7B7B7", fill = "#F7B7B7")+
theme(axis.text.x.left = element_text(size = 5))+
coord_flip()+
scale_y_continuous(name = "Number of Complaints", labels = scales::comma) +
scale_x_discrete(name = "Companies") +
labs(title = "Companies x Debt Collection Related Complaints",
subtitle = "2020") +
theme_minimal()
ggplotly(Top_db)
```